LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models

• URL: http://arxiv.org/abs/2305.13655v3
• Date: Mon, 4 Mar 2024 18:43:49 GMT
• Title: LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models
• Authors: Long Lian, Boyi Li, Adam Yala, Trevor Darrell
• Abstract summary: This work proposes to enhance prompt understanding capabilities in text-to-image diffusion models. Our method leverages a pretrained large language model for grounded generation in a novel two-stage process. Our method significantly outperforms the base diffusion model and several strong baselines in accurately generating images.
• Score: 62.75006608940132
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in text-to-image diffusion models have yielded impressive results in generating realistic and diverse images. However, these models still struggle with complex prompts, such as those that involve numeracy and spatial reasoning. This work proposes to enhance prompt understanding capabilities in diffusion models. Our method leverages a pretrained large language model (LLM) for grounded generation in a novel two-stage process. In the first stage, the LLM generates a scene layout that comprises captioned bounding boxes from a given prompt describing the desired image. In the second stage, a novel controller guides an off-the-shelf diffusion model for layout-grounded image generation. Both stages utilize existing pretrained models without additional model parameter optimization. Our method significantly outperforms the base diffusion model and several strong baselines in accurately generating images according to prompts that require various capabilities, doubling the generation accuracy across four tasks on average. Furthermore, our method enables instruction-based multi-round scene specification and can handle prompts in languages not supported by the underlying diffusion model. We anticipate that our method will unleash users' creativity by accurately following more complex prompts. Our code, demo, and benchmark are available at: https://llm-grounded-diffusion.github.io

Related papers

• FreeSeg-Diff: Training-Free Open-Vocabulary Segmentation with Diffusion Models [56.71672127740099]
  We focus on the task of image segmentation, which is traditionally solved by training models on closed-vocabulary datasets. We leverage different and relatively small-sized, open-source foundation models for zero-shot open-vocabulary segmentation. Our approach (dubbed FreeSeg-Diff), which does not rely on any training, outperforms many training-based approaches on both Pascal VOC and COCO datasets.
  arXiv  Detail & Related papers  (2024-03-29T10:38:25Z)
• DreamDistribution: Prompt Distribution Learning for Text-to-Image Diffusion Models [53.17454737232668]
  We introduce a solution that allows a pretrained T2I diffusion model to learn a set of soft prompts. These prompts offer text-guided editing capabilities and additional flexibility in controlling variation and mixing between multiple distributions. We also show the adaptability of the learned prompt distribution to other tasks, such as text-to-3D.
  arXiv  Detail & Related papers  (2023-12-21T12:11:00Z)
• Reason out Your Layout: Evoking the Layout Master from Large Language Models for Text-to-Image Synthesis [47.27044390204868]
  We introduce a novel approach to improving T2I diffusion models using Large Language Models (LLMs) as layout generators. Our experiments demonstrate significant improvements in image quality and layout accuracy.
  arXiv  Detail & Related papers  (2023-11-28T14:51:13Z)
• Reverse Stable Diffusion: What prompt was used to generate this image? [73.10116197883303]
  We study the task of predicting the prompt embedding given an image generated by a generative diffusion model. We propose a novel learning framework comprising a joint prompt regression and multi-label vocabulary classification objective. We conduct experiments on the DiffusionDB data set, predicting text prompts from images generated by Stable Diffusion.
  arXiv  Detail & Related papers  (2023-08-02T23:39:29Z)
• SUR-adapter: Enhancing Text-to-Image Pre-trained Diffusion Models with Large Language Models [56.88192537044364]
  We propose a simple-yet-effective parameter-efficient fine-tuning approach called the Semantic Understanding and Reasoning adapter (SUR-adapter) for pre-trained diffusion models. Our approach can make text-to-image diffusion models easier to use with better user experience.
  arXiv  Detail & Related papers  (2023-05-09T05:48:38Z)
• In-Context Learning Unlocked for Diffusion Models [163.54453915874402]
  We present Prompt Diffusion, a framework for enabling in-context learning in diffusion-based generative models. We propose a vision-language prompt that can model a wide range of vision-language tasks and a diffusion model that takes it as input. The resulting Prompt Diffusion model is the first diffusion-based vision-language foundation model capable of in-context learning.
  arXiv  Detail & Related papers  (2023-05-01T23:03:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.